On this page, you'll find several examples of prompts that follow the TASBO format. You will see prompt examples from one of the FREE versions of AI models, such as Microsoft CoPilot, Google Gemini, ChatGPT, and Meta.ai. No prompt or AI model ever generates the same result twice, so copy-n-paste the prompts or make up your own, then try them out on the AI model of your choice.
TASBO Prompt Engineering Steps
Tailor your prompts to the specific AI model and task at hand.
Articulate your instructions clearly and concisely, providing all necessary context and details.
Simplify complex tasks by breaking them down into smaller, bite-sized steps.
Balance the amount of information given. Avoid overwhelming AI.
Optimize via testing and refinement.
See examples
Example #1: Budget Forecasting
Analyze the current budget and forecast future expenses for our school district.
Prompt: Using the provided budget data for the current fiscal year, generate a detailed analysis of our school district's financial status. Based on historical trends and projected changes, forecast our anticipated expenses for the next three years, broken down by category. Provide a summary of your findings and highlight any areas of concern.
Example #2: Cost Saving Ideas
Suggest cost-saving strategies for our school's operational budget.
Prompt: Examine our school's operational budget for the current year. Identify areas where we may be able to reduce costs without compromising the quality of education. Provide a list of specific cost-saving strategies, along with a brief explanation of how each could be implemented and the potential savings associated with each strategy.
Prompt Analysis and Breakdown
Tailoring to the specific task of suggesting cost-saving strategies for the school's operational budget.
Clear articulation of instructions: examine current year's budget, identify cost reduction areas without compromising education quality, and provide a list of strategies with explanations and potential savings.
Simplification of the complex task into smaller steps: examine budget, identify areas, provide strategies, explain implementation and savings.
Balancing information by specifying the current year's budget and desired output without overwhelming the AI.
Example #3: Charts
Generate a chart comparing our district's financial performance to state averages.
Prompt: Using the most recent financial data available for our district and the state averages, create a clear and concise chart comparing our district's performance across key metrics such as per-student spending, revenue sources, and budget allocation. Highlight areas where our district is performing above or below the state average, and provide brief insights into the potential reasons for these differences.
Prompt Analysis and Breakdown
Tailor your prompts to the specific AI model and task at hand. The prompt is tailored to the task of analyzing the current budget and forecasting future expenses for the school district, which is one of the specific scenarios provided.
Articulate your instructions clearly and concisely, providing all necessary context and details. The instructions are clear and concise, specifying the use of the current fiscal year's budget data, the need for a detailed analysis, and the requirement to forecast expenses for the next three years. It also asks for a summary of findings and areas of concern.
Simplify complex tasks by breaking them down into smaller, bite-sized steps. The prompt breaks down the complex task of budget analysis and forecasting into smaller steps: a. Analyze the current budget data b. Generate a detailed analysis of the district's financial status c. Forecast anticipated expenses for the next three years, broken down by category d. Provide a summary of findings and highlight areas of concern
Balance the amount of information given. Avoid overwhelming AI. The prompt provides enough information for the AI to understand the task at hand without overwhelming it with unnecessary details. It specifies the data to be used (current fiscal year's budget), the time frame for forecasting (next three years), and the desired output (detailed analysis, expense forecast, summary of findings, and areas of concern).
Optimize via testing and refinement. This step would involve testing the prompt with the AI model and refining it based on the output received. As this is a hypothetical scenario, this step cannot be demonstrated in this context.